Information processing for model building of an electric power system, based on experimental data Kozmin Stanislav. State National Research Polytechnic.

Similar presentations

Presentation on theme: "Information processing for model building of an electric power system, based on experimental data Kozmin Stanislav. State National Research Polytechnic."— Presentation transcript:

1
Information processing for model building of an electric power system, based on experimental data Kozmin Stanislav. State National Research Polytechnic University of Perm

2
The Gas turbine plant (GTP) The Gas turbine plant (GTP) consists of two main parts: the power turbine and the generator. They are placed in a single package. The high temperature gas flow affects on turbine blades (creates a torque). GTP in the energy sector operating as basic mode, as to cover peak loads. There is a gas turbine model, which allows to simulate the processes occurring in the plant, its parameters and direct the process of generating electricity.

3
State National Research Polytechnic University of Perm The main problem The problem is that the modeling of the 5 seconds of the process takes 50 seconds of real time. In order to bring the work of the model to real-time, we need to do identification again and simplify the identified object. GTP has been divided into two parts for identification: the gas turbine and the synchronous generator.

4
Goals and Objectives State National Research Polytechnic University of Perm Familiarity with installation; Carrying out the experiment; Choosing the appropriate method of identification; Definition the identification algorithm; Developing application for processing data, conducting calculations, checking calculations; Check the model for adequacy.

5
Identifying model State National Research Polytechnic University of Perm Combined identification of the generator and loading, AROE&A - an automatic regulator of excitation and the activator. Combined identification of the generator and loading concerning pressure and active power, АROE&A - an automatic regulator of excitation and the activator.

6
Technology State National Research Polytechnic University of Perm Y - the vector of output variables, X - the vector of input variables, A - the coefficient matrix, the dimension n × n, which should be identified Y Σ and X Σ - matrix consisting of n vectors Y and X, respectively. If we have more than n observations - the method of least squares in the following notation: V (k) - the extended state vector in the k-point time moment, V (k +1) - the extended state vector in the k +1-point time moment, F - transition matrix from state at time k in the new state the time k +1,, Δt - time interval between the time points k and k +1. The vector V includes both input and output variables of the identified system. Transform the system (1) to the difference (discrete) form. Consider the method of identification. Take the simplest method - least squares.

7
The identification algorithm State National Research Polytechnic University of Perm

8
Implementation of the method and the result State National Research Polytechnic University of Perm The structure of the identification model is: U - effective voltage, P - active power, U f - excitation voltage, ω - angular speed of GTP - generator

9
Implementation of the method and the result State National Research Polytechnic University of Perm Figure 4. The Graph of transient process of power derived from the experiment. Figure 5. The Graph of transient process of power derived from a model with a step of 0.5s.

10
Implementation of the method and the result State National Research Polytechnic University of Perm Figure 6. The Graph of transient process of the effective voltage derived from the experiment. Figure 7. The Graph of transient process of effective voltage derived from a model with a step of 0.5s.

11
Conclusion State National Research Polytechnic University of Perm As a result of the research was founded that if we will take a lot of points for identification, the adequacy of the model will go down. However, the quality that we received when checking the adequacy of the model, allow us to say that the resulting model of the electric power system is adequate. If it is necessary to increase the accuracy of the model it is advisable to increase the count of points. And it is advisable to use additional parameters for the calculation that indirectly affect the object. Since the dimension of the data matrix increases, it improves the adequacy of the system.